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Sequential Monte Carlo with transformations. [PDF]

open access: yesStat Comput, 2020
AbstractThis paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions ...
Everitt RG   +3 more
europepmc   +8 more sources

Parameter estimation from aggregate observations: a Wasserstein distance-based sequential Monte Carlo sampler [PDF]

open access: yesRoyal Society Open Science, 2023
In this work, we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data.
Chen Cheng, Linjie Wen, Jinglai Li
doaj   +2 more sources

Sequential Monte Carlo-guided ensemble tracking. [PDF]

open access: yesPLoS ONE, 2017
A great deal of robustness is allowed when visual tracking is considered as a classification problem. This paper combines a finite number of weak classifiers in a SMC framework as a strong classifier.
Yuru Wang   +4 more
doaj   +2 more sources

Sequential Monte Carlo samplers [PDF]

open access: yesJournal of the Royal Statistical Society Series B: Statistical Methodology, 2006
SummaryWe propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods.
Ajay Jasra   +2 more
exaly   +4 more sources

Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo [PDF]

open access: yesBayesian Analysis, 2021
Sequential Monte Carlo (SMC) samplers form an attractive alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rejuvenate particles. We discuss how to calibrate automatically (using the current particles) Hamiltonian Monte Carlo kernels within SMC.
Nicolas Chopin, Pierre Jacob
exaly   +4 more sources

Sequential Quasi Monte Carlo [PDF]

open access: yesJournal of the Royal Statistical Society Series B: Statistical Methodology, 2015
SummaryWe derive and study sequential quasi Monte Carlo (SQMC), a class of algorithms obtained by introducing QMC point sets in particle filtering. SQMC is related to, and may be seen as an extension of, the array-RQMC algorithm of L'Ecuyer and his colleagues. The complexity of SQMC is O{Nlog(N)}, where N is the number of simulations at each iteration,
Mathieu Gerber, Nicolas Chopin
exaly   +5 more sources

SeqClone: sequential Monte Carlo based inference of tumor subclones [PDF]

open access: yesBMC Bioinformatics, 2019
Background Tumor samples are heterogeneous. They consist of varying cell populations or subclones and each subclone is characterized with a distinct single nucleotide variant (SNV) profile.
Oyetunji E. Ogundijo, Xiaodong Wang
doaj   +2 more sources

Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach [PDF]

open access: yesPeerJ, 2018
Tumor samples obtained from a single cancer patient spatially or temporally often consist of varying cell populations, each harboring distinct mutations that uniquely characterize its genome.
Oyetunji E. Ogundijo, Xiaodong Wang
doaj   +3 more sources

A sequential Monte Carlo approach to gene expression deconvolution. [PDF]

open access: yesPLoS ONE, 2017
High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of ...
Oyetunji E Ogundijo, Xiaodong Wang
doaj   +2 more sources

Time-Varying GPS Displacement Network Modeling by Sequential Monte Carlo [PDF]

open access: yesEntropy
Geodetic observations through high-rate GPS time-series data allow the precise modeling of slow ground deformation at the millimeter level. However, significant attention has been devoted to utilizing these data for various earth science applications ...
Suchanun Piriyasatit   +2 more
doaj   +2 more sources

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